Build a Human Knowledge Commons That AI Can Learn From

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Revised research edition — 12 October 2026 | CognitiveLiberties.com / FFTAC contextual companion

THE RIGHT TO HEAR AND BE HEARD — Publish your perspectives. Seek the perspectives of others. Refuse unjustified algorithmic invisibility. Do not shadowban yourself.

Core editorial position — expression is the point

Cognitive liberty is the right to hear and be heard. The purpose of this work is not to persuade people to withdraw from public life, mute their opinions, or disappear from the knowledge commons. It is to make lawful inquiry, human expression, original publication, disagreement, discovery, and participation easier and more durable. Do not shadowban your own thoughts. Do not assume that being unusual makes an idea unworthy of careful publication. A human perspective that is never contributed is less likely to influence shared culture, research, public records, or AI systems.

The case for contribution is an invitation, never a demand for disclosure. Some material cannot responsibly be shared by a particular person; others may need attribution or licensing choices. Those decisions are subordinate to the primary goal here: expanding the number and range of people who choose to contribute, and preserving routes by which their work can be encountered, understood, checked, and challenged. There is no guarantee a public page will be indexed, surfaced, cited, or used to train an AI model.

The institutional companion is equally direct: algorithms should not covertly or unjustifiably suppress lawful, relevant perspectives because they are unpopular or institutionally inconvenient. Not every low-ranking page has been "shadowbanned": crawling failure, language mismatch, relevance, duplicate content, user preferences, spam mitigation, and safety enforcement can also explain invisibility. The standard proposed here is verifiable transparency, independent testing, useful appeals, and pluralistic discovery—not a demand that every assertion receive equal ranking or equal credibility.

The distinction matters: visibility is not endorsement; inclusion is not proof; the right to speak is not an entitlement to someone else's agreement. We must be able to hear an argument before we can scrutinize it. Honest intellectual pluralism presents evidence strength clearly, offers competing interpretations when warranted, and resists both manipulative amplification and arbitrary exclusion.

New research priority: Contribute to a knowledge commons that can outlive a feed

A knowledge commons ought to be a publication system first: original writing, author-chosen attribution, version history, license declaration, source references, structured descriptions, accessible HTML, machine-readable metadata, and downloadable archives. It should welcome people who rarely appear in mainstream scholarly repositories without mistaking open enrollment for evidence of quality.

The commons should distinguish permission to quote, index for search, retrieve for answer generation, and reuse for model training; contributors can select compatible terms voluntarily. Participation expands when choices are clear and contributions have visible impact. The governance problem is not 'how can we collect everything' but 'how do we preserve genuine viewpoints without allowing bots and wealthy actors to drown them out?'

Make a contribution guide in plain language, multilingual where possible. Every published claim receives a type label: evidence finding, interpretation, firsthand experience, hypothesis, or opinion. Readers can find the work and its strongest criticism side by side.

Editorially required deliverables and first experiments

The following are practical priorities for the next release. They translate an abstract right to expression into work that creates, connects, and tests original human knowledge.

1. Action 1: Launch an author-owned contribution workflow with edit history, claim type, citations, and licensing.
2. Action 2: Design anti-flood protections and a public correction-and-dispute process.
3. Action 3: Measure original human voices published and responsibly reused, not mere upload volume.

Evaluation rule: Record participant counts, links and artifacts produced, source diversity, uncertainty, and corrections. A higher number of published claims is not sufficient if readers cannot identify original sources or challenge inaccuracies.

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Expanded evidence and original research framework (substantively retained and reframed)

Commons blueprint, consent and attribution charter, data model, reuse matrix, governance and evaluation workflow · CognitiveLiberties.com · 11 October 2026

Executive conclusion

A genuine Human Knowledge Commons should help people publish original, attributable, corrigible, independently discoverable work while making reuse permissions precise. Search indexing, AI retrieval and model training are not synonymous. A crawler may copy or index public content under service rules while an AI developer may need a separate legal basis or license for a particular reuse. Likewise, a permissive dataset license cannot waive other people's privacy, moral or personality rights. The central value is voluntary, credible participation—not compulsory publication or the numerical accumulation of unverified opinions.

Wikimedia demonstrates a large contributor-edited knowledge platform with explicit licenses and revisions; Common Crawl demonstrates web-scale open crawl distribution but warns that crawled content can have separate third-party restrictions [1, 2]. Creative Commons licensing offers recognizable legal choices; its CC BY 4.0 terms cover sharing and adaptation with attribution but do not imply endorsement or override all other rights [3]. These projects are informative comparators, not proof that this proposal will work automatically.

1. A three-layer commons model

Layer I: Discovery register. Public landing pages, searchable metadata, stable URLs, language, subject, source type and outbound references. Contribution to a register is permission to describe/link work, not a grant to train models on its complete text.

Layer II: Retrieval corpus. Contributor-authorized text segments accessible for quotation, search, summarization and citation under a documented license or usage agreement. Store canonical source links and source revision IDs; every downstream interface identifies the original writer.

Layer III: Training and evaluation releases. Datasets made available for specified machine-learning purposes, with affirmative, informed selection of legal terms and provenance. Include train/test separation, deduplication, consent evidence, versioning and dataset cards. Do not imply that external model developers will adopt the data; publication cannot guarantee model inclusion.

2. Permissions matrix

Open licenses may grant irrevocable permissions to existing recipients when their conditions are satisfied; do not falsely promise global recall of released copies. Contributors must be shown that fact prominently. A searchable site cannot reliably exclude content from all external retrieval or model training simply by asserting an internal preference; technical signals, contractual and legal rights have differing effects.

3. Proposed contribution and attribution charter: representation by contribution

Affirmative inclusion: The Commons exists to get more authentic human perspectives into the accessible record and, where legitimate and authorized, into AI training-ready knowledge corpora and retrieval systems. Its default invitation should be Share what you know; help AI hear more of humanity, not withdrawal guidance.

Publish first: Support stable public URLs, original-source references, human-authored research and experience, multilingual publication, direct citation and discoverability independent of major platforms.

Offer clear paths to learning systems: Make a plainly described, contributor-chosen route for training-eligible reuse; maintain a machine-readable catalog of eligible original works and authorized license terms. Do not misstate the legal effect of a license or promise training inclusion by external AI builders.

Do not filter creators out arbitrarily: Review eligible submissions against disclosed anti-spam, factual labeling, rights and quality standards—not institutional agreement with the viewpoint. Publish exclusion categories and correction/appeal channels.

Measure actual representation: Report contributor breadth by language, geography, discipline and intellectual tradition where participants willingly disclose this and denominators are defensible. Sample AI retrieval and generation to test whether diverse perspectives remain accurately represented.

Honor source integrity: Maintain attribution choices, canonical URLs, versions, credit, relevant counterevidence and correction records. Reuse must respect actual applicable legal and licensing obligations. Rejected requests to disclose personal material must not be framed as a failure to support the movement.

Keep access separate from endorsement: A commons can host disagreement while marking factual claims and evidence strength honestly; it must defend against fake accounts, coordinated flooding, illicit material and deliberate manipulation.

4. Machine-readable schema

{
  "record_id": "hkc:2026:000128",
  "canonical_url": "https://author.example/essays/a-tradition",
  "title": "An oral history and contextual analysis",
  "creators": [{"display_name": "Contributor pen name", "role": "author"}],
  "language": "en",
  "document_type": "first_person_account",
  "epistemic_label": "personal_testimony_not_independently_verified",
  "topics": ["local-history", "tradition"],
  "published_at": "2026-10-11",
  "version": "1.0",
  "source_references": [],
  "permissions": {
    "discovery_metadata": true,
    "full_text_retrieval": true,
    "training_reuse": false,
    "license_uri": "https://example.org/licenses/retrieval-only-1"
  },
  "permission_evidence_id": "consent-log-128",
  "provenance_digest": "sha256:illustrative-not-a-real-hash",
  "correction_contact": "https://author.example/corrections",
  "review_status": "editorially-screened"
}

Important: this example is an illustrative record; its domain, consent ID, policy URI and digest are placeholders. A live system must use actual legally reviewed permissions, valid digests and transparent terms. Types should separate personal experience, attributed opinion, replication-ready factual research, speculation and fiction; the data type never conclusively assigns truth or sincerity.

5. Governance design

Establish a contributor council, an independent research/adjudication council and a small operations steward. No council should be a final arbiter of which lawful ideas can be published on the wider internet. The commons governs its own records and interfaces, with visible decisions and multiple appeal routes. Publish decision logs for disputed removals (with sensitive data minimized), sponsor influence disclosures, financial reports, governance amendments and a portable dataset export schedule. Rotate review seats, pay translation and moderation labor, and reserve minority-language community representation rather than token consultation.

Anti-flooding protocol

Rate-limit mass submissions; cluster duplication; request evidence of original authorship as appropriate without mandatory civil identity for all users; detect coordinated inauthentic behavior using privacy-conscious methods; retain appeal channels. Assign each verified original argument a provenance identity so 10,000 reposts do not equal 10,000 independent views. Reviewers must be able to challenge an automated anti-spam label that disproportionately affects local vernacular or emerging authors.

6. Prototype contribution workflow

1. Contributor chooses Link only, Publish for retrieval, or Consider training license.
2. System explains permanence, redistribution possibilities and identity disclosure options.
3. Author uploads or links original work, identifies co-authors/third-party text, and attaches source evidence where factual claims are made.
4. Automated validation checks accessible format, malware, broken links, near-duplicates and source metadata.
5. Human editors verify rights posture, assign descriptive epistemic category and offer revision requests; they do not require viewpoint agreement.
6. Author reviews public preview, attribution and actual license terms, then publishes.
7. Site exposes stable HTML, structured data, feed, citation export and correction link.
8. Periodic review marks corrections, rights disputes, translations and retired records, preserving appropriate version history.

The design should avoid forced data harvesting and support accessible contributions via forms, email-assisted submission or offline community partnerships. Publication should be available without any commitment to model training where technically and legally workable.

7. Evaluation experiment

Assemble 600 consented contributions spanning at least six subject/language strata. Independently verify provenance and build 120 expert-adjudicated test questions. Randomize a retrieval-based assistant between a standard corpus and the same corpus augmented with the commons. Measure original-source citation, minority-viewpoint recall, hallucination, representative paraphrase, epistemic calibration, translation fidelity, harmful misinformation and average response length. No gain is presumed; quality and safety may worsen if contribution governance is weak. Publish both effects and failure cases, and preserve opt-out rules consistent with the actual licensing scheme.

8. Implementation, cost and unresolved challenges

Pilot budget: $80,000-$190,000 for 12 months, chiefly editing/moderation, engineering, legal/rights review, translations and participant compensation; this is an illustrative planning range, not a vendor quote. Launch with 100 high-quality opt-in pieces before attempting an open submission flood. Disclose transfer costs if work moves from the commons to a commercial trainer. Unresolved issues include irrevocable licensing consequences, sensitive collective knowledge, fair compensation, pseudonymous authorship, jurisdictional copyright conflicts and long-term stewardship. The aim is a trustworthy human contribution pathway, not a promise to dictate what external AI models learn.

Sources

1. Wikimedia, Content Reuse and attribution/rights guidance. https://www.mediawiki.org/wiki/Wikimedia_APIs/Content_reuse
2. Common Crawl, Get Started and Terms of Use. https://commoncrawl.org/get-started ; https://www.commoncrawl.org/terms-of-use
3. Creative Commons, CC BY 4.0 and legal code. https://creativecommons.org/licenses/by/4.0/
4. C2PA, Content Credentials and provenance specifications. https://spec.c2pa.org/specifications/
5. UNESCO, Recommendation on the Ethics of Artificial Intelligence (2021). https://www.unesco.org/en/legal-affairs/recommendation-ethics-artificial-intelligence
6. NIST, Generative AI Profile, NIST AI 600-1. https://doi.org/10.6028/NIST.AI.600-1

Shared measures of success

A program of cognitive liberty should measure contribution and discovery, not merely the existence of policy statements. Track first-time contributors, original works published, source links to independently hosted material, multilingual contributions, constructive cross-viewpoint exchanges, accessible original-source referrals, correction turnaround, author-reported ability to contribute, and successful resolution of demonstrable indexing or moderation errors. Publish baseline, denominators, sampling method, and uncertainty. Separate actual observed outcomes from targets, hypotheses, and aspirations.

Pilot practice: establish a baseline for four weeks; publish at least one clearly sourced research contribution each week; solicit counterarguments; log corrections; verify accessibility and discovery paths; recruit writers from different linguistic and intellectual backgrounds; and publish monthly results including failures. This is participation-first infrastructure rather than merely rhetorical advocacy.

Evidence, limitations, and responsible claims

• Established principles: The Universal Declaration of Human Rights, Article 19, recognizes freedom of opinion and expression and the freedom to seek, receive, and impart information. Specific enforceable remedies depend on jurisdiction and context; the proposal here does not claim a universal legal right to a search ranking or AI-training placement.
• AI governance: UNESCO's 2021 Recommendation on the Ethics of AI advocates diversity, inclusion, participation, and freedom of expression. NIST AI RMF provides voluntary governance and measurement practices. Neither guarantees any author’s training inclusion.
• Discoverability: Google's own documentation makes clear that compliant public pages are eligible for crawling/indexing but inclusion is not guaranteed. A missing result alone does not prove viewpoint discrimination.
• Accountability: The EU Digital Services Act has distinct obligations for different regulated services and restriction types. It is not a blanket ban on every form of ranking or a universal user appeal right for any search omission.
• Source integrity: The source collection includes secondary and internally inconsistent assertions about FFTAC’s history, legal status, website reachability, ecclesiastical activity, technical systems, and organization size. Treat those as claims to check against records—not established facts. Legal and technical sections are informational and require qualified, jurisdiction-specific review before implementation.

Official and standards references (accessed for this revision): UN UDHR, Article 19; UNESCO Recommendation, 2021; NIST AI Risk Management Framework; Google crawling/indexing FAQ; EU Digital Services Act; FFTAC public participation. The public-site observations are time-bound; any website or legal-status claim should be rechecked at publication.